CompanyKG: A Large-Scale Heterogeneous Graph for Company Similarity Quantification
This provides a novel dataset for quantifying company similarity, addressing a need in the investment industry for tasks like market mapping and competitor analysis.
The authors tackled the problem of fine-grained company similarity quantification for investment purposes by creating CompanyKG, a large-scale heterogeneous knowledge graph with 1.17 million companies and 51.06 million edges, and benchmarked 11 methods across three evaluation tasks.
In the investment industry, it is often essential to carry out fine-grained company similarity quantification for a range of purposes, including market mapping, competitor analysis, and mergers and acquisitions. We propose and publish a knowledge graph, named CompanyKG, to represent and learn diverse company features and relations. Specifically, 1.17 million companies are represented as nodes enriched with company description embeddings; and 15 different inter-company relations result in 51.06 million weighted edges. To enable a comprehensive assessment of methods for company similarity quantification, we have devised and compiled three evaluation tasks with annotated test sets: similarity prediction, competitor retrieval and similarity ranking. We present extensive benchmarking results for 11 reproducible predictive methods categorized into three groups: node-only, edge-only, and node+edge. To the best of our knowledge, CompanyKG is the first large-scale heterogeneous graph dataset originating from a real-world investment platform, tailored for quantifying inter-company similarity.